Big Data vs Data Science: The 15 Significant Key Differences To Know

Every organization with or without profit generates a vast
amount of data for the execution of their plans. When a big amount
of data occurs in a dataset that is called big data. All types of
data, structured or unstructured, in any format can appear in big
data. Taking about data science, it is the method of processing big
data without considering if the dataset is structured or
unstructured. It uses the algorithms and scientific methods for the
analysis of data. The main focus of data science is to extract
knowledge from any big data. This article explains big data vs data
science to provide a better overview.

Big Data vs Data Science:
Significant Key Differences

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Big data and data science are not the same at all and
people must differ by their working process and meaning. While
focusing on big data vs data science we found out 15 important
things people must know to be clarified of why big data and
data
science^[1] are interrelated but
separate.

1. What Do
They Mean?

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There are some characteristics that can determine the
dataset if big data or not. Volume determines the quantity of data
consisting of insights of an exact event. Variety stands for the
variation of data in a dataset. This determines the identity of
data and helps to find out more detailed and potential information
about an event. Velocity indicates the continuous growth of the
event or organization and determines how fast the data are being
generated.

Data science is a scientific method based program that
works on big data by using its algorithm. It excerpts important
information from various kinds of data and directly or indirectly
participates in the decision making of an event or organization or
a company that generates big data. Data science^[2]
is mostly similar to data mining as both of these audits on a
database to get new, unique, and important knowledge from the
dataset processing and analyzing it.

2. Big Data vs Data Science:
Perception

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Big data is generally generated from various data sources.
So, big data can be called a collective dataset. Every type and
format of data is possible to add in big data, as the dataset is
made with data from different sources. Structured or unstructured
or even semi-structured datasets can be big data. An organization
or company basically generates real-time data that ensures the
current status of an event and helps them work accordingly towards
the goal.

Data science involves various techniques and tools for
analyzing a dataset. The main concept of data science is to
simplify the complexity of big data. It is a concept that was made
to lessen the hassle in taking decisions for a company. Talking
about big data vs data science, Big data^[3]
are generally unstructured and need to be simplified and data
science is the faster solution to it than the traditional
applications.

3. Sources and Formation

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Big data generally a compile of
gathered knowledge from various sources. In most cases, data are
compiled from traffics on the Internet or the usage history of
Internet users. Live streams, E-devices are also two major sources
of data compilation. Besides, databases, excel files, or e-commerce
history play the most major role as sources for organizations.
Dealings are done through emails that create important history for
the company and data gets included in the dataset.

Data science is the scientific method that analysis data
arrange them accordingly and filter unwanted and uneven unreal data
from big data. It gains an idea about the event from the dataset
and processes the dataset according to the company model and
creates a model using those data accumulating all the data that are
important. It helps to activate applications processing necessary
data and creating models for the application to make it work fast
and provide accuracy.

4. Fields of Operation

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Big data are generally needed in events where data is
generated continuously and mostly in real-time. Big multinational
companies and governmental organizations mostly in focus produce
more data. Big data works in fields related to
health^[4], e-commerce, businesses,
and so on. The generation of data is seen in the areas where law,
regulation, and security issues as well are present.
Telecommunication is a big source where big data are generated as
thousands of history are created.

Data Science has many fields to implement its algorithms
and finds the best result of the event. Comparing big data vs data
science, searching history on the Internet is a major source of big
data generation and data science works to find out the result such
as user preferences, visited websites, etc. It works in recognition
of speech or image, digital contents, spam or risk detection, and
helps to analyze big data for and from the development of a
website.

5. Why and How

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Big data helps to bring mobility in the workforce of a
company. In this world full of competitors the businesses must be
combative and without big data its unimaginable. It helps
businesses to grow and get the expected result out of the
investment. With the group of data from various sources, it helps
the authority to take the next move thoroughly showing every
possible data that are produced during different transactions and
other involving deals.

Focusing on big data vs data science, data science is the
only solution to take out the findings from big data with the help
of mathematical algorithms. Another characteristic is the
statistical tool that emphasizes the big data so that businesses
can find more proper and accurate steps to move. Data science
performs as a data visualization tool^[5]
predicting the result, preparing model, damaging and also
processing data, and helping an event to provide the maximum
output.

6. Big Data vs Data Science:
Tools

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Since big data
was first introduced in 2005 by Roger Mougalas for the
company O’Reilly Media it developed many new and interesting tools
that process big data. As an example, we can focus on Hadoop^[6]
by Apache that distributes huge data on different computers, and
for this, it just needs to follow the plain design of programming.
Other tools, in addition, are Apache Spark, Apache
Cassandra which work for SQL, graph procession, scalability, and so
on.

Data science since its invention is working for various
companies for easing the decision making and fastening it as well.
Within these years data scientists have developed the topic data
science with various tools. Python programming^[7], R programming^[8], Tableau, Excel are some
big and very common examples with what data science can be
explained. Statistical explanation and exponential growth curves
with the probability of an event can also be shown with these
tools.

7. Big Data vs Data Science:
Impacts

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Big data has a bigger impact on the businesses that were
started at an early age when the term wasn’t even introduced. When
big data took the responsibility of Walmart, where tons of products
are sold on a regular basis, with a term called a retail link, the
products came under a database and every product was a single data.
However, it also boosts the companies that generate more data and
maximum IT companies are based on their data.

Data science shows the light to any business enlightening
the data from an unknown pattern to known. It helps to explore
newer ways during decision making, develop processes, and expand
the profits through product improvisation. When any wrong comes in
between any event, data science helps to identify the cause and
provides solutions sometimes as well. UPS delivery system uses data
science for making profits and providing the best quality customer
support analyzing all the real-time data.

8. Platforms

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In big data vs data science, big data is generally
produced from every possible history that can be made in an event.
Big data workers find it very appreciating for a company and so
they started to think about smoother and faster production of big
data. As a result, different platforms started the operation of
producing big data. Enlightening examples can be Microsoft Machine
Learning Server, Cloudera, DOMO, Hortonworks, Vertica, Kofax
Insight, AgilOne, and many more.

Data science works for the improvement of a company
through data analysis, process, preparation, etc. Realizing the
importance and the use of data science, scientists started working
on it to create the most detailed and accurate data science
platform. After several attempts, many platforms got created and
analyzing the faulty the next one got created with the solution to
the faulty. As examples, MATLAB^[9], TIBCO Statistica,
Anaconda^[10], H20, R-Studio,
Databricks Unified Analytics Platform, etc are notable.

9. Relation with Cloud
Computing

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The
objective of big data is to serve as CEO and achieve business
success and cloud computing’s objective is to serve as CIO in
providing a convenient and accurate IT solution. When the bid data
and cloud computing work together, business and IT-related success
come quickly and the productivity becomes smoother and faster. Big
data can be stored on a cloud as cloud computing^[11] provides a lot of
storage and big data needs the storage to get stored as
well.

Working with data science it is needed to apply algorithms
to find out the accurate result and cut out unnecessary data. Not
all the time it is possible to do with regular offline computers.
Clouds are advantaged with high computational requirements and data
storage. Data science needs bigger storage to store the analyzed
data. Cloud computing is the only easier solution to this and with
its help, the computing specification for data analysis is also
met.

10. Relation with IoT

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Big
data, in general, are generated normally, and in a structured
pattern. But when big data are created on IoT, it is often
unstructured or sometimes you may find it semi-structured. As there
are a variety of data, necessary or unnecessary, the big data are
different from the regular big data and the dataset is only usable
when analyzed. According to HP, IoT is going to be a big part of
big data with high-growth in volume.

Data science works in a different on IoT based big data
than the regular. Big data of IoT is generally produced in
real-time. So the result that comes out is the most updated. Though
it helps to make the best effort with its intelligence, it’s a
little harder to analyze the big data. Without the specialized
skills of data scientists its almost impossible to figure out the
unsegregated unnecessary data from the set and process as
needed.

11. Relation with Artificial
Intelligence

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AI is
just like human intelligence in the form of machines. As it works
as a decision-maker it needs to generate a huge amount of data and
this dataset is called big data. Big data in Artificial intelligence^[12] are used to identify
the pattern of data distribution and it helps to detect
irregularity. Graphs and probability are the studies for knowing
the status showing the relational growths and it is only possible
with real-time data generated for AI.

Data science works in where data are available especially
big data. As AI produces big data and the data are mostly generated
in real-time, data science uses its algorithm on it. Depending on
the produced data after being analyzed, the data science tool
provides a solution, decision, and outlook. Exemplifying the IBM
Watson that assistances the doctors with complete fast solution
based on the history of a patient. It reduces the workload for the
workforce.

12. Future Prospect

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In the future, big data will make a huge difference in
every field. It will bring opportunities for the educated
unemployed with the offer of the post of chief data officer. Laws
by different leading organizations will be implemented for data
security. As 93% of data remains untouched and treated as
unnecessary data it will be used with importance in the coming
days. But the challenges of storing the huge data are coming as
well.

Data science is going to be the next big giant in the
coming days. It is going to make more data scientists attracting
them to data science and its opportunities. Companies are now badly
in need of data scientists^[13] for the analysis of
their data. The search on the Internet will become even better,
smoother, and faster to the users as a result of the upgraded data
science. Coding will be less important for data
analysis.

13. Concentrates On

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Big data generally focus on technical issues. It gets
generated from any important or unimportant source. It extracts all
the data from a source and includes it in a dataset. This is how
the data becomes huge in amount and we call it big data. When the
data is generated there is no restriction to exclude data. This
mostly extracted real-time data are the main key for a company
though most of the data remain untouched.

Data science works with the algorithm, statistics,
probability, mathematics, etc. The main focus of data science is on
the decision making of a business. Businesses are becoming
competitive and everyone wants to come out as a winner. Data
scientists are highly paid for the role and they are a part of the
decision-maker as well. This decision making is the main key for a
business to gain success in its own field competing
others.

14. Data Filtering

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In big data vs data
science, big data basically gets bigger and bigger and it never
stops growing. But it can help to identify the data which
are most important and which are lest important. This is called the
data cleansing process. But as the dataset is consisting of huge
data it is very difficult to find out the detected data and analyze
it by ownself. Though it is a harder process, big data help in data
cleaning through error data detection.

Data science is used to find out the error and clean it.
Data science when applied to big data, helps in processing,
analyzing, outputting a final result. In this way, the summary of
big data comes out and the unnecessary data remains untouched.
These untouched data are not needed anymore and can be cleaned. And
this is how data science helps to keep the Internet clean removing
unnecessary, corrupted data and finding out the errors.

15. Authentication Funnel

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Big data vs data science can be explained when it comes to
design patterns. Before adding data to big data, first, the data is
identified in the data source and gets under filtration and
validation test. After that, if the data is noisy it comes under
detected and the noise is reduced and then the conversion of data
takes place. Being compressed the data gets integrated. This is how
the overall design pattern of big data and how it works.

In the data science design pattern, firstly, the formulas
or laws are applied to a dataset, then the problem with the data
gets detected. The solution to the problem that was found must be
got for proceeding to the next step. Any advantages attached to the
data is found out in the next step. Then the uses of the data must
be found out and finally relating to other models the sample code
is implemented.

Finally, Insight

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Big data and data science are two big giants of this era
of competitors. Every business is each other’s competitor. To win
in the race one needs to produce meaningful data and analyze it
with data science for better decision making. Through this decision
making the next move will to the light and newer exceptional ways
come in the light as well. The exponential growth will take place
and the growth of the economy and IT sector will be
eye-catching.